@article{Zhang2025, 
author = {Zhihao Zhang and Xin Xin and Yong Zhou and Daifeng Dang and Jiahao Fu and Yanfeng Liu},
title = {An operational regulation method for solar heating system based on Deep reinforcement learning},
year = {2025},
journal = {Building Simulation},
volume = {18},
number = {8},
pages = {1945-1961},
keywords = {solar heating system, model-free predictive control, optimal control, Deep reinforcement learning},
url = {https://www.sciopen.com/article/10.1007/s12273-025-1297-8},
doi = {10.1007/s12273-025-1297-8},
abstract = {The performance of solar heating systems is significantly influenced by outdoor weather fluctuations and building heating loads, leading to dynamic variations that undermine the efficacy of rule-based control (RBC) strategies. Additionally, the hydraulic and thermal time-delay characteristics frequently lead to delays in control points for real-time optimization (RTO) control strategies. While Model Predictive Control (MPC) effectively addresses these dynamic and time-delay issues in solar heating systems, its substantial computational demands limit its real-world applications. To overcome these challenges, this study proposes a Model-Free Predictive Control (MFPC) approach utilizing Deep reinforcement learning (DRL). Through TRNSYS simulations, the study conducts a comparison of the performance and energy consumption of RBC and MFPC systems, focusing on a residential solar heating system in Lhasa, Xizang as a case study. The results demonstrate that the MFPC method reduces unmet heating demand by 31% compared to traditional RBC strategies, improves solar collection efficiency by nearly 12%, and decreases tank heat loss by 2.2%. When accounting for thermal storage effects, the optimized MFPC strategy achieves a reduction in net energy consumption of 25.6%.}
}